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<span id="Basic-distributed_002dtranspose-interface"></span><div class="header">
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<hr>
<span id="Basic-distributed_002dtranspose-interface-1"></span><h4 class="subsection">6.7.1 Basic distributed-transpose interface</h4>

<p>In particular, suppose that we have an <code>n0</code> by <code>n1</code> array in
row-major order, block-distributed across the <code>n0</code> dimension.  To
transpose this into an <code>n1</code> by <code>n0</code> array block-distributed
across the <code>n1</code> dimension, we would create a plan by calling the
following function:
</p>
<div class="example">
<pre class="example">fftw_plan fftw_mpi_plan_transpose(ptrdiff_t n0, ptrdiff_t n1,
                                  double *in, double *out,
                                  MPI_Comm comm, unsigned flags);
</pre></div>
<span id="index-fftw_005fmpi_005fplan_005ftranspose"></span>

<p>The input and output arrays (<code>in</code> and <code>out</code>) can be the
same.  The transpose is actually executed by calling
<code>fftw_execute</code> on the plan, as usual.
<span id="index-fftw_005fexecute-3"></span>
</p>

<p>The <code>flags</code> are the usual FFTW planner flags, but support
two additional flags: <code>FFTW_MPI_TRANSPOSED_OUT</code> and/or
<code>FFTW_MPI_TRANSPOSED_IN</code>.  What these flags indicate, for
transpose plans, is that the output and/or input, respectively, are
<em>locally</em> transposed.  That is, on each process input data is
normally stored as a <code>local_n0</code> by <code>n1</code> array in row-major
order, but for an <code>FFTW_MPI_TRANSPOSED_IN</code> plan the input data is
stored as <code>n1</code> by <code>local_n0</code> in row-major order.  Similarly,
<code>FFTW_MPI_TRANSPOSED_OUT</code> means that the output is <code>n0</code> by
<code>local_n1</code> instead of <code>local_n1</code> by <code>n0</code>.
<span id="index-FFTW_005fMPI_005fTRANSPOSED_005fOUT-1"></span>
<span id="index-FFTW_005fMPI_005fTRANSPOSED_005fIN-1"></span>
</p>

<p>To determine the local size of the array on each process before and
after the transpose, as well as the amount of storage that must be
allocated, one should call <code>fftw_mpi_local_size_2d_transposed</code>,
just as for a 2d DFT as described in the previous section:
<span id="index-data-distribution-4"></span>
</p>
<div class="example">
<pre class="example">ptrdiff_t fftw_mpi_local_size_2d_transposed
                (ptrdiff_t n0, ptrdiff_t n1, MPI_Comm comm,
                 ptrdiff_t *local_n0, ptrdiff_t *local_0_start,
                 ptrdiff_t *local_n1, ptrdiff_t *local_1_start);
</pre></div>
<span id="index-fftw_005fmpi_005flocal_005fsize_005f2d_005ftransposed"></span>

<p>Again, the return value is the local storage to allocate, which in
this case is the number of <em>real</em> (<code>double</code>) values rather
than complex numbers as in the previous examples.
</p>
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